Output relevant fault reconstruction and fault subspace extraction in total projection to latent structures models

Industrial and Engineering Chemistry Research

Volumn 49, Issue 19, 2010, Pages 9175-9183

  Li, Gang ^a   Joe Qin, S ^b   Zhou, Donghua ^a  

^a TSINGHUA UNIVERSITY   (China)

^b University of Southern California ^*  (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DETECTABILITY; FAULT DIRECTION; FAULT MAGNITUDES; FAULT RECONSTRUCTION; INDUSTRIAL PROCESSS; OUTPUT DATA; PROCESS DATA; PROJECTION TO LATENT STRUCTURES; QUALITY OF PRODUCT; SIMULATION EXAMPLE; STATISTICAL DATAS; TENNESSEE EASTMAN PROCESS;

PROCESS MONITORING;

EID: 77957561573     PISSN: 08885885     EISSN: 15205045     Source Type: Journal    
DOI: 10.1021/ie901939n     Document Type: Article

References (22)

1. Kresta, J. V.; Macgregor, J. F.; Marlin, T. E. Multivariate statistical monitoring of process operating performance Can. J. Chem. Eng. 1991, 69, 35-47
2. Macgregor, J. F.; Kourti, T. Statistical process control of multivariate processes Control Eng. Pract. 1995, 3, 403-414
3. Wise, B. M.; Gallagher, N. B. The process chemometrics approach to process monitoring and fault detection J. Process Control 1996, 6, 329-348
4. Raich, A.; Cinar, A. Statistical process monitoring and disturbance diagnosis in multivariable continuous processes AIChE J. 1996, 42, 995-1009
5. Dunia, R.; Joe Qin, S. Joint diagnosis of process and sensor faults using principal component analysis Control Eng. Practice 1998, 6, 457-469
6. Yue, H.; Qin, S. Reconstruction-Based Fault Identification Using a Combined Index Ind. Eng. Chem. Res. 2001, 40, 4403-4414
7. Dunia, R.; Qin, S. J.; Edgar, T. F.; McAvoy, T. J. Identification of faulty sensors using principal component analysis AIChE J. 1996, 42, 2797-2812
8. Dunia, R.; Qin, S. J. Subspace approach to multidimensional fault identification and reconstruction AIChE J. 1998, 44, 1813-1831
9. Qin, S. J. Statistical process monitoring: basics and beyond J. Chemom. 2003, 17, 480-502
10. Geladi, P.; Kowalski, B. R. Partial least-squares regression: a tutorial Anal. Chim. Acta 1986, 185, 1-17
11. Hóskuldsson, A. PLS regression methods J. Chemom. 1988, 2, 211-228
12. MacGregor, J. F.; Jaeckle, C.; Kiparissides, C.; Koutoudi, M. Process monitoring and diagnosis by multiblock PLS methods AIChE J. 1994, 40, 826-838
13. Li, G.; Qin, S. J.; Zhou, D. H. Geometric properties of partial least squares for process monitoring Automatica 2010, 46, 204-210
14. Zhou, D. H.; Li, G.; Qin, S. J. Total projection to latent structures for process monitoring AIChE J. 2010, 56, 168-178
15. Li, G.; Zhou, D. H.; Ji, Y. D.; Qin, S. J. Total PLS based contribution plots for fault diagnosis Acta Automat. Sin. 2009, 35, 759-765
16. Valle, S.;; Qin, S.;; Piovoso, M.;; Bachmann, M.;; Mandakoro, N. Extracting fault subspaces for fault identification of a polyesterfilm process. Proceedings ACC, Arlington, VA, June 25-27, 2001; pp 4466 - 4471.
17. Wold, S.; Antti, H.; Lindgren, F.; öhman, J. Orthogonal signal correction of near-infrared spectra Chemom. Intell. Lab. Syst. 1998, 44, 175-185
18. Trygg, J.; Wold, S. Orthogonal projections to latent structures (O-PLS) J. Chemom. 2002, 16, 119-128
19. Box, G. E. P. Some theorems on quadratic forms applied in the study of analysis of variance problems, I. Effect of inequality of variance in the one-way classification Ann. Math. Stat. 1954, 25, 290-302
20. Downs, J. J.; Vogel, E. F. A plant-wide industrial process control problem Comput. Chem. Eng. 1993, 17, 245-255
21. Chiang, L. H.; Russell, E.; Braatz, R. D. Fault detection and diagnosis in industrial systems; Springer: London, 2001.
22. Lee, G.; Han, C. H.; Yoon, E. S. Multiple-Fault Diagnosis of the Tennessee Eastman Process Based on System Decomposition and Dynamic PLS Ind. Eng. Chem. Res. 2004, 43, 8037-8048